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        Package&nbsp;trunk ::
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        Class&nbsp;Dream
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<!-- ==================== CLASS DESCRIPTION ==================== -->
<h1 class="epydoc">Class Dream</h1><p class="nomargin-top"><span class="codelink"><a href="trunk.BIP.Bayes.Samplers.MCMC-pysrc.html#Dream">source&nbsp;code</a></span></p>
<pre class="base-tree">
object --+    
         |    
  <a href="trunk.BIP.Bayes.Samplers.MCMC._Sampler-class.html" onclick="show_private();">_Sampler</a> --+
             |
            <strong class="uidshort">Dream</strong>
</pre>

<hr />
DiffeRential Evolution Adaptive Markov chain sampler

<!-- ==================== INSTANCE METHODS ==================== -->
<a name="section-InstanceMethods"></a>
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      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a href="trunk.BIP.Bayes.Samplers.MCMC.Dream-class.html#__init__" class="summary-sig-name">__init__</a>(<span class="summary-sig-arg">self</span>,
        <span class="summary-sig-arg">meldobj</span>,
        <span class="summary-sig-arg">samples</span>,
        <span class="summary-sig-arg">sampmax</span>,
        <span class="summary-sig-arg">data</span>,
        <span class="summary-sig-arg">t</span>,
        <span class="summary-sig-arg">parpriors</span>,
        <span class="summary-sig-arg">parnames</span>,
        <span class="summary-sig-arg">parlimits</span>,
        <span class="summary-sig-arg">likfun</span>,
        <span class="summary-sig-arg">likvariance</span>,
        <span class="summary-sig-arg">burnin</span>,
        <span class="summary-sig-arg">thin</span>=<span class="summary-sig-default">5</span>,
        <span class="summary-sig-arg">convergenceCriteria</span>=<span class="summary-sig-default">1.1</span>,
        <span class="summary-sig-arg">nCR</span>=<span class="summary-sig-default">3</span>,
        <span class="summary-sig-arg">DEpairs</span>=<span class="summary-sig-default">1</span>,
        <span class="summary-sig-arg">adaptationRate</span>=<span class="summary-sig-default">.65</span>,
        <span class="summary-sig-arg">eps</span>=<span class="summary-sig-default">5e-6</span>,
        <span class="summary-sig-arg">mConvergence</span>=<span class="summary-sig-default">False</span>,
        <span class="summary-sig-arg">mAccept</span>=<span class="summary-sig-default">False</span>,
        <span class="summary-sig-arg">**kwargs</span>)</span><br />
      x.__init__(...) initializes x; see x.__class__.__doc__ for signature</td>
          <td align="right" valign="top">
            <span class="codelink"><a href="trunk.BIP.Bayes.Samplers.MCMC-pysrc.html#Dream.__init__">source&nbsp;code</a></span>
            
          </td>
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    </td>
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    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a name="_det_outlier_chains"></a><span class="summary-sig-name">_det_outlier_chains</span>(<span class="summary-sig-arg">self</span>,
        <span class="summary-sig-arg">step</span>)</span><br />
      Determine which chains are outliers</td>
          <td align="right" valign="top">
            <span class="codelink"><a href="trunk.BIP.Bayes.Samplers.MCMC-pysrc.html#Dream._det_outlier_chains">source&nbsp;code</a></span>
            
          </td>
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    </td>
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    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a name="delayed_rejection"></a><span class="summary-sig-name">delayed_rejection</span>(<span class="summary-sig-arg">self</span>,
        <span class="summary-sig-arg">xi</span>,
        <span class="summary-sig-arg">zi</span>,
        <span class="summary-sig-arg">pxi</span>,
        <span class="summary-sig-arg">zprob</span>)</span><br />
      Generates a second proposal based on rejected proposal xi</td>
          <td align="right" valign="top">
            <span class="codelink"><a href="trunk.BIP.Bayes.Samplers.MCMC-pysrc.html#Dream.delayed_rejection">source&nbsp;code</a></span>
            
          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr class="private">
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a href="trunk.BIP.Bayes.Samplers.MCMC.Dream-class.html#_alpha1" class="summary-sig-name" onclick="show_private();">_alpha1</a>(<span class="summary-sig-arg">self</span>,
        <span class="summary-sig-arg">p1</span>,
        <span class="summary-sig-arg">p2</span>)</span><br />
      Returns the Metropolis acceptance probability:
alpha1(p1,p1) = min(1,p1/p2) if p2 &gt;-np.inf else 1</td>
          <td align="right" valign="top">
            <span class="codelink"><a href="trunk.BIP.Bayes.Samplers.MCMC-pysrc.html#Dream._alpha1">source&nbsp;code</a></span>
            
          </td>
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      <span class="summary-type">&nbsp;</span>
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      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a name="update_CR_dist"></a><span class="summary-sig-name">update_CR_dist</span>(<span class="summary-sig-arg">self</span>)</span></td>
          <td align="right" valign="top">
            <span class="codelink"><a href="trunk.BIP.Bayes.Samplers.MCMC-pysrc.html#Dream.update_CR_dist">source&nbsp;code</a></span>
            
          </td>
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    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a name="_prop_initial_theta"></a><span class="summary-sig-name">_prop_initial_theta</span>(<span class="summary-sig-arg">self</span>,
        <span class="summary-sig-arg">step</span>)</span><br />
      Generate Theta proposals from priors</td>
          <td align="right" valign="top">
            <span class="codelink"><a href="trunk.BIP.Bayes.Samplers.MCMC-pysrc.html#Dream._prop_initial_theta">source&nbsp;code</a></span>
            
          </td>
        </tr>
      </table>
      
    </td>
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    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a name="_prop_phi"></a><span class="summary-sig-name">_prop_phi</span>(<span class="summary-sig-arg">self</span>,
        <span class="summary-sig-arg">thetalist</span>,
        <span class="summary-sig-arg">po</span>=<span class="summary-sig-default">None</span>)</span><br />
      Returns proposed Phi derived from theta</td>
          <td align="right" valign="top">
            <span class="codelink"><a href="trunk.BIP.Bayes.Samplers.MCMC-pysrc.html#Dream._prop_phi">source&nbsp;code</a></span>
            
          </td>
        </tr>
      </table>
      
    </td>
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    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a name="_chain_evolution"></a><span class="summary-sig-name">_chain_evolution</span>(<span class="summary-sig-arg">self</span>,
        <span class="summary-sig-arg">proptheta</span>,
        <span class="summary-sig-arg">propphi</span>,
        <span class="summary-sig-arg">pps</span>,
        <span class="summary-sig-arg">liks</span>)</span><br />
      Chain evolution as describe in ter Braak's Dream algorithm.</td>
          <td align="right" valign="top">
            <span class="codelink"><a href="trunk.BIP.Bayes.Samplers.MCMC-pysrc.html#Dream._chain_evolution">source&nbsp;code</a></span>
            
          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr class="private">
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a href="trunk.BIP.Bayes.Samplers.MCMC.Dream-class.html#_get_post_prob" class="summary-sig-name" onclick="show_private();">_get_post_prob</a>(<span class="summary-sig-arg">self</span>,
        <span class="summary-sig-arg">theta</span>,
        <span class="summary-sig-arg">prop</span>,
        <span class="summary-sig-arg">po</span>=<span class="summary-sig-default">None</span>)</span><br />
      Calculates the posterior probability for the proposal of each chain</td>
          <td align="right" valign="top">
            <span class="codelink"><a href="trunk.BIP.Bayes.Samplers.MCMC-pysrc.html#Dream._get_post_prob">source&nbsp;code</a></span>
            
          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a name="step"></a><span class="summary-sig-name">step</span>(<span class="summary-sig-arg">self</span>)</span><br />
      Does the actual sampling loop.</td>
          <td align="right" valign="top">
            <span class="codelink"><a href="trunk.BIP.Bayes.Samplers.MCMC-pysrc.html#Dream.step">source&nbsp;code</a></span>
            
          </td>
        </tr>
      </table>
      
    </td>
  </tr>
  <tr>
    <td colspan="2" class="summary">
    <p class="indent-wrapped-lines"><b>Inherited from <code><a href="trunk.BIP.Bayes.Samplers.MCMC._Sampler-class.html" onclick="show_private();">_Sampler</a></code></b>:
      <code><a href="trunk.BIP.Bayes.Samplers.MCMC._Sampler-class.html#DIC">DIC</a></code>,
      <code><a href="trunk.BIP.Bayes.Samplers.MCMC._Sampler-class.html#best_prop_index">best_prop_index</a></code>,
      <code><a href="trunk.BIP.Bayes.Samplers.MCMC._Sampler-class.html#check_constraints">check_constraints</a></code>,
      <code><a href="trunk.BIP.Bayes.Samplers.MCMC._Sampler-class.html#dimensions">dimensions</a></code>,
      <code><a href="trunk.BIP.Bayes.Samplers.MCMC._Sampler-class.html#gr_R">gr_R</a></code>,
      <code><a href="trunk.BIP.Bayes.Samplers.MCMC._Sampler-class.html#gr_convergence">gr_convergence</a></code>,
      <code><a href="trunk.BIP.Bayes.Samplers.MCMC._Sampler-class.html#po">po</a></code>,
      <code><a href="trunk.BIP.Bayes.Samplers.MCMC._Sampler-class.html#setup_xmlrpc_plotserver">setup_xmlrpc_plotserver</a></code>,
      <code><a href="trunk.BIP.Bayes.Samplers.MCMC._Sampler-class.html#shut_down">shut_down</a></code>,
      <code><a href="trunk.BIP.Bayes.Samplers.MCMC._Sampler-class.html#shutdown_xmlrpc_plotserver">shutdown_xmlrpc_plotserver</a></code>,
      <code><a href="trunk.BIP.Bayes.Samplers.MCMC._Sampler-class.html#term_pool">term_pool</a></code>
      </p>
    <div class="private">    <p class="indent-wrapped-lines"><b>Inherited from <code><a href="trunk.BIP.Bayes.Samplers.MCMC._Sampler-class.html" onclick="show_private();">_Sampler</a></code></b> (private):
      <code><a href="trunk.BIP.Bayes.Samplers.MCMC._Sampler-class.html#_accept" onclick="show_private();">_accept</a></code>,
      <code><a href="trunk.BIP.Bayes.Samplers.MCMC._Sampler-class.html#_every_plot" onclick="show_private();">_every_plot</a></code>,
      <code><a href="trunk.BIP.Bayes.Samplers.MCMC._Sampler-class.html#_propose" onclick="show_private();">_propose</a></code>,
      <code><a href="trunk.BIP.Bayes.Samplers.MCMC._Sampler-class.html#_tune_likvar" onclick="show_private();">_tune_likvar</a></code>,
      <code><a href="trunk.BIP.Bayes.Samplers.MCMC._Sampler-class.html#_watch_chain" onclick="show_private();">_watch_chain</a></code>
      </p></div>
    <p class="indent-wrapped-lines"><b>Inherited from <code>object</code></b>:
      <code>__delattr__</code>,
      <code>__format__</code>,
      <code>__getattribute__</code>,
      <code>__hash__</code>,
      <code>__new__</code>,
      <code>__reduce__</code>,
      <code>__reduce_ex__</code>,
      <code>__repr__</code>,
      <code>__setattr__</code>,
      <code>__sizeof__</code>,
      <code>__str__</code>,
      <code>__subclasshook__</code>
      </p>
    </td>
  </tr>
</table>
<!-- ==================== CLASS VARIABLES ==================== -->
<a name="section-ClassVariables"></a>
<table class="summary" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr bgcolor="#70b0f0" class="table-header">
  <td colspan="2" class="table-header">
    <table border="0" cellpadding="0" cellspacing="0" width="100%">
      <tr valign="top">
        <td align="left"><span class="table-header">Class Variables</span></td>
        <td align="right" valign="top"
         ><span class="options">[<a href="#section-ClassVariables"
         class="privatelink" onclick="toggle_private();"
         >hide private</a>]</span></td>
      </tr>
    </table>
  </td>
</tr>
  <tr>
    <td colspan="2" class="summary">
    <p class="indent-wrapped-lines"><b>Inherited from <code><a href="trunk.BIP.Bayes.Samplers.MCMC._Sampler-class.html" onclick="show_private();">_Sampler</a></code></b>:
      <code><a href="trunk.BIP.Bayes.Samplers.MCMC._Sampler-class.html#e">e</a></code>,
      <code><a href="trunk.BIP.Bayes.Samplers.MCMC._Sampler-class.html#liklist">liklist</a></code>,
      <code><a href="trunk.BIP.Bayes.Samplers.MCMC._Sampler-class.html#seqhist">seqhist</a></code>,
      <code><a href="trunk.BIP.Bayes.Samplers.MCMC._Sampler-class.html#trace_acceptance">trace_acceptance</a></code>,
      <code><a href="trunk.BIP.Bayes.Samplers.MCMC._Sampler-class.html#trace_convergence">trace_convergence</a></code>
      </p>
    <div class="private">    <p class="indent-wrapped-lines"><b>Inherited from <code><a href="trunk.BIP.Bayes.Samplers.MCMC._Sampler-class.html" onclick="show_private();">_Sampler</a></code></b> (private):
      <code><a href="trunk.BIP.Bayes.Samplers.MCMC._Sampler-class.html#_R" onclick="show_private();">_R</a></code>,
      <code><a href="trunk.BIP.Bayes.Samplers.MCMC._Sampler-class.html#_dimensions" onclick="show_private();">_dimensions</a></code>,
      <code><a href="trunk.BIP.Bayes.Samplers.MCMC._Sampler-class.html#_j" onclick="show_private();">_j</a></code>,
      <code><a href="trunk.BIP.Bayes.Samplers.MCMC._Sampler-class.html#_po" onclick="show_private();">_po</a></code>
      </p></div>
    </td>
  </tr>
</table>
<!-- ==================== PROPERTIES ==================== -->
<a name="section-Properties"></a>
<table class="summary" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr bgcolor="#70b0f0" class="table-header">
  <td colspan="2" class="table-header">
    <table border="0" cellpadding="0" cellspacing="0" width="100%">
      <tr valign="top">
        <td align="left"><span class="table-header">Properties</span></td>
        <td align="right" valign="top"
         ><span class="options">[<a href="#section-Properties"
         class="privatelink" onclick="toggle_private();"
         >hide private</a>]</span></td>
      </tr>
    </table>
  </td>
</tr>
  <tr>
    <td colspan="2" class="summary">
    <p class="indent-wrapped-lines"><b>Inherited from <code>object</code></b>:
      <code>__class__</code>
      </p>
    </td>
  </tr>
</table>
<!-- ==================== METHOD DETAILS ==================== -->
<a name="section-MethodDetails"></a>
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       cellspacing="0" width="100%" bgcolor="white">
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        <td align="left"><span class="table-header">Method Details</span></td>
        <td align="right" valign="top"
         ><span class="options">[<a href="#section-MethodDetails"
         class="privatelink" onclick="toggle_private();"
         >hide private</a>]</span></td>
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  </td>
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</table>
<a name="__init__"></a>
<div>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <table width="100%" cellpadding="0" cellspacing="0" border="0">
  <tr valign="top"><td>
  <h3 class="epydoc"><span class="sig"><span class="sig-name">__init__</span>(<span class="sig-arg">self</span>,
        <span class="sig-arg">meldobj</span>,
        <span class="sig-arg">samples</span>,
        <span class="sig-arg">sampmax</span>,
        <span class="sig-arg">data</span>,
        <span class="sig-arg">t</span>,
        <span class="sig-arg">parpriors</span>,
        <span class="sig-arg">parnames</span>,
        <span class="sig-arg">parlimits</span>,
        <span class="sig-arg">likfun</span>,
        <span class="sig-arg">likvariance</span>,
        <span class="sig-arg">burnin</span>,
        <span class="sig-arg">thin</span>=<span class="sig-default">5</span>,
        <span class="sig-arg">convergenceCriteria</span>=<span class="sig-default">1.1</span>,
        <span class="sig-arg">nCR</span>=<span class="sig-default">3</span>,
        <span class="sig-arg">DEpairs</span>=<span class="sig-default">1</span>,
        <span class="sig-arg">adaptationRate</span>=<span class="sig-default">.65</span>,
        <span class="sig-arg">eps</span>=<span class="sig-default">5e-6</span>,
        <span class="sig-arg">mConvergence</span>=<span class="sig-default">False</span>,
        <span class="sig-arg">mAccept</span>=<span class="sig-default">False</span>,
        <span class="sig-arg">**kwargs</span>)</span>
    <br /><em class="fname">(Constructor)</em>
  </h3>
  </td><td align="right" valign="top"
    ><span class="codelink"><a href="trunk.BIP.Bayes.Samplers.MCMC-pysrc.html#Dream.__init__">source&nbsp;code</a></span>&nbsp;
    </td>
  </tr></table>
  
  <p>x.__init__(...) initializes x; see x.__class__.__doc__ for 
  signature</p>
  <dl class="fields">
    <dt>Overrides:
        object.__init__
        <dd><em class="note">(inherited documentation)</em></dd>
    </dt>
  </dl>
</td></tr></table>
</div>
<a name="_alpha1"></a>
<div class="private">
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <table width="100%" cellpadding="0" cellspacing="0" border="0">
  <tr valign="top"><td>
  <h3 class="epydoc"><span class="sig"><span class="sig-name">_alpha1</span>(<span class="sig-arg">self</span>,
        <span class="sig-arg">p1</span>,
        <span class="sig-arg">p2</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    ><span class="codelink"><a href="trunk.BIP.Bayes.Samplers.MCMC-pysrc.html#Dream._alpha1">source&nbsp;code</a></span>&nbsp;
    </td>
  </tr></table>
  
  Returns the Metropolis acceptance probability:
alpha1(p1,p1) = min(1,p1/p2) if p2 &gt;-np.inf else 1
  <dl class="fields">
    <dt>Parameters:</dt>
    <dd><ul class="nomargin-top">
        <li><strong class="pname"><code>p1</code></strong> - : log probability</li>
        <li><strong class="pname"><code>p2</code></strong> - : log probability</li>
    </ul></dd>
    <dt>Decorators:</dt>
    <dd><ul class="nomargin-top">
        <li><code>@np.vectorize</code></li>
    </ul></dd>
  </dl>
</td></tr></table>
</div>
<a name="_get_post_prob"></a>
<div class="private">
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <table width="100%" cellpadding="0" cellspacing="0" border="0">
  <tr valign="top"><td>
  <h3 class="epydoc"><span class="sig"><span class="sig-name">_get_post_prob</span>(<span class="sig-arg">self</span>,
        <span class="sig-arg">theta</span>,
        <span class="sig-arg">prop</span>,
        <span class="sig-arg">po</span>=<span class="sig-default">None</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    ><span class="codelink"><a href="trunk.BIP.Bayes.Samplers.MCMC-pysrc.html#Dream._get_post_prob">source&nbsp;code</a></span>&nbsp;
    </td>
  </tr></table>
  
  Calculates the posterior probability for the proposal of each chain
  <dl class="fields">
    <dt>Parameters:</dt>
    <dd><ul class="nomargin-top">
        <li><strong class="pname"><code>theta</code></strong> - : list of nchains thetas</li>
        <li><strong class="pname"><code>prop</code></strong> - : list of nchains phis</li>
        <li><strong class="pname"><code>po</code></strong> - : Pool of processes</li>
    </ul></dd>
    <dt>Returns:</dt>
        <dd><ul class="rst-simple">
<li><code class="link">posts</code>: list of log posterior probabilities of length self.nchains</li>
<li><code class="link">listoliks</code>: list of log-likelihoods of length self.nchains</li>
</ul></dd>
  </dl>
</td></tr></table>
</div>
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